mrmc {MRMCaov}

Multi-Reader Multi-Case ROC Analysis

Description

Estimation and comparison of ROC performance metrics for multi-reader multi-case studies.

Usage

mrmc(response, test, reader, case, data, cov = jackknife, design = NULL)

Arguments

response

response metric expressed in terms of a package-supplied performance metric.

test

variable of test identifiers.

reader

variable of reader identifiers.

case

variable of case identifiers.

data

data frame containing the response, test, reader, and case variables.

cov

function, function call, or character string naming the method to use in calculating performance metric covariances.

design

one of the following study designs: 1 = factorial, 2 = cases nested within readers, 3 = cases nested within tests, or NULL to automatically set the design based on variable codings in data.

Details

Readers and cases are treated as random factors by default. Either one may be designated as fixed in calls to mrmc with the syntax fixed(<variable name>), where <variable name> is the name of the reader or case variable.

Value

Returns an mrmc class object with the following elements.

design

experimental study design: 1 = factorial, 2 = cases nested within readers, 3 = cases nested within tests.

vars

character names of the analysis factors and reader performance metric.

fixed

logicals indicating whether the reader and case factors are treated as fixed in the analysis.

aov

results from an ordinary analysis of variance.

data

data frame of computed reader performance metrics for the analysis of variance.

num_obs

number of case observations for each of the computed metrics.

cov

reader performance covariance matrix.

mrmc_data

data frame of case-specific reader ratings.

levels

character levels of the true case statuses.

References

Dorfman DD, Berbaum KS, and Metz CE (1992). Receiver operating characteristic rating analysis. Generalization to the population of readers and patients with the jackknife method. Investigative Radiology, 27: 723–731.

Obuchowski NA and Rockette HE (1995). Hypothesis testing of diagnostic accuracy for multiple readers and multiple tests: an ANOVA approach with dependent observations. Communications in Statistics–Simulation and Computation 24: 285–308.

Hillis SL, Obuchowski NA, Schartz KM, and Berbaum KS (2005). A comparison of the Dorfman-Berbaum-Metz and Obuchowski-Rockette methods for receiver operating characteristic (ROC) data. Statisticsin Medicine, 24: 1579–1607.

Hillis SL (2007). A comparison of denominator degrees of freedom methods for multiple observer ROC analysis. Statistics in Medicine, 26: 596–619.

Hillis SL, Berbaum KS, and Metz CE (2008). Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis. Academic Radiology, 15: 647–661.

See Also

metrics, cov_methods, parameters, plot, roc_curves, summary

Examples

## Random readers and cases
(est <- mrmc(empirical_auc(truth, rating), treatment, reader, case,
             data = VanDyke))
plot(est)
summary(est)

## Fixed readers and random cases
est <- mrmc(empirical_auc(truth, rating), treatment, fixed(reader), case,
            data = VanDyke)
summary(est)